Search Results (5)

Achieving high-precision spatiotemporal synchronization is crucial for the implementation of virtual coupling (VC) in railway systems. This paper proposes a moving baseline real-time kinematic (MB-RTK) framework to enhance relative positioning accuracy and synchronization robustness between coupled trains. By leveraging global navigation satellite system (GNSS) carrier-phase differential processing and dynamic baseline estimation, MB-RTK effectively mitigates positioning errors caused by GNSS signal degradation, multipath interference, and synchronization latency, ensuring stable and reliable inter-train coordination. The proposed framework was evaluated through comprehensive simulations and field experiments. The results demonstrate that MB-RTK achieves centimeter-level relative positioning accuracy under normal GNSS conditions, maintains tracking errors within 10 m, and typically keeps velocity synchronization deviations within ±0.5 km/h. Furthermore, the RTK status analysis reveals that NARROW_INT provides the highest stability, while continuous RTK corrections are essential to ensure seamless synchronization in dynamic environments. To further enhance synchronization performance, a decentralized distributed synchronization algorithm was introduced, reducing communication overhead and improving real-time responsiveness. The proposed approach exhibits strong resilience to GNSS disruptions, making it well-suited for high-density and autonomous train operations. Overall, this study highlights MB-RTK as a promising solution for VC applications, offering high accuracy, low latency, and strong adaptability in complex railway scenarios. Future research will focus on AI-driven dynamic corrections, integration with complementary localization methods, and large-scale deployment strategies to further optimize the system’s robustness and scalability. Full article

Real-time kinematic (RTK) positioning of the global navigation satellite systems (GNSS) is used to provide centimeter-level positioning accuracy. There are several ways to implement RTK but a Kalman filter-based RTK is preferred because of its superior capability to resolve GNSS carrier phase integer ambiguities. However, the positioning performance of the Kalman filter-based RTK is often compromised by various factors when it comes to determining a precise relative position vector between moving unmanned aerial vehicles (UAVs) equipped with low-cost GNSS receivers and antennas, where the locations of both GNSS antennas are not accurately known and change over time. Some of the critical factors that lead to a high rate of incorrect resolutions of carrier phase integer ambiguities are measurement time differences between GNSS receivers, frequent cycle slips with high noise in code and carrier phase measurements, and an improper Kalman filter gain due to a newly risen satellite. In this paper, effective methods to deal with those factors to achieve a seamless Kalman filter-based RTK performance in moving UAVs are presented. Using our extensive 45 flight tests data sets, conducted over a duration of 3 to 12 min, the RTK positioning results showed that the root-mean-square position error (RMSE) decreased by up to 95.13%, with an average of 65.31%, and that the percentage of epochs that passed the ratio test, which is the most common method for validating double differenced carrier phase integer ambiguity resolution, increased by up to 130%, with an average of 23.54%. Full article

To address problems such as pesticide poisoning and accidents during pest control work and to enable efficient work in this area, the development of a competitively prices speed sprayer with autonomous driving is required. Accordingly, in order to contribute to developing the commercialization of a low-cost autonomous driving speed sprayer, we developed a positioning algorithm and an autonomous driving-based spraying algorithm by using two low-cost global navigation satellite system (GNSS) modules and a low-cost motion sensor. In order to provide stable navigation solutions from the autonomous driving hardware despite disturbances from the electromagnetic field generated by the spraying device, the proposed positioning algorithm, a moving baseline (MB) real-time kinematic (RTK)/motion sensor-integrated positioning algorithm, was developed using a loosely coupled extended Kalman filter. To compare the yaw estimation performance provided by the MB RTK positioning technique, yaw was calculated by post-processing with two types of positioning algorithms: the MB RTK/motion sensor-integrated positioning algorithm and the GNSS RTK/motion sensor-integrated positioning algorithm. In the static test, the precision of the yaw provided by the MB RTK/motion sensor-integrated positioning algorithm was 0.14°, but with the GNSS RTK/motion sensor-integrated positioning algorithm, the precision of the yaw was 4.53°. The static test results confirmed that the proposed positioning algorithm using the yaw provided by the MB RTK positioning technique based on two GNSS modules for measurement, precisely estimated the yaw even when the spray engine was operating. To perform autonomous driving and spraying, an autonomous driving-based spraying algorithm was developed using the MB RTK/motion sensor-integrated positioning algorithm. As a result of two performance tests based on the proposed algorithm in an orchard, autonomous driving and spraying were stably performed according to the set autonomous driving route and spraying method, and the root mean square (RMS) of the path-following error was 0.06 m. Full article

The global navigation satellite system (GNSS) real-time kinematic (RTK) technique is used to achieve relative positioning centimeter levels among multiple agents on the move. A typical GNSS RTK estimates the relative positions of multiple rover receivers with respect to a single-base receiver. In a fleet of rover GNSS receivers, this approach is inefficient because each rover receiver only uses GNSS measurements of its own and those sent from a single-base receiver. In this study, we propose a novel GNSS RTK framework that facilitates the precise positioning of a swarm of moving vehicles through the GNSS measurements of multiple receivers and broadcasts fixed-integer ambiguities of GNSS carrier phases. The proposed framework not only provides efficient RTK positioning but also reliable performance with a limited number of GNSS satellites in view. Our experimental flight tests with six GNSS receivers showed that the systematic procedure of the proposed framework could maintain lower than 6 cm of 3D RMS positioning errors, whereas the conventional RTK failed to resolve the correct integer ambiguities of double difference carrier phase measurements more than 13% in five out of nine total baselines. Full article

The purpose of this publication is to analyze the spatial and temporal variability of the territorial sea baseline in sand bottom waterbodies, which were determined twice, in 2016 and 2018, by the Real Time Kinematic (RTK) method. This involves direct measurement of sea bottom coordinates on planned hydrographic sounding profiles using a Global Navigation Satellite System (GNSS) receiver mounted on a pole. The data were the basis for creating Digital Terrain Models (DTM), which were then used to determine the baseline for both measurement campaigns. Subsequently, terrain surface models were compared to determine bathymetry changes in the area under analysis, and an assessment was made of the baseline spatial position change over the previous two years. The measurements have shown considerable spatial and temporal variability of the baseline course along a short section of sandy beach. The territorial sea baseline was very unstable; in some places, it moved by even 20–25 m, landwards and seawards. Therefore, one can suppose that these changes are periodic, and one can conclude that the reliability of the baseline measurements can decrease quite quickly. Full article